Yang, Ivana V.

Abstract Rationale Idiopathic pulmonary fibrosis (IPF) is a complex and heterogeneous disease. Given this, we reasoned that differences in genetic profiles may be associated with unique clinical and radiologic features. Computational image analysis, sometimes referred to as radiomics, provides objective, quantitative assessments of radiologic features in subjects with pulmonary fibrosis. Objectives To determine if the genetic risk profile of patients with IPF identifies unique computational imaging phenotypes. Methods Participants with IPF were included in this study if they had genotype data and computed tomography (CT) scans of the chest available for computational image analysis. The extent of lung fibrosis and the likelihood of a usual interstitial pneumonia (UIP) pattern were scored automatically using two separate, previously validated deep learning techniques for CT analysis. UIP pattern was also classified visually by radiologists according to established criteria. Results Among 329 participants with IPF, MUC5B and ZKSCAN1 were independently associated with the deep learning–based UIP score. None of the common variants were associated with fibrosis extent by computational imaging. We did not find an association between MUC5B or ZKSCAN1 and visually assessed UIP pattern. Conclusions Select genetic variants are associated with computer-based classification of UIP on CT in this IPF cohort. Analysis of radiologic features using deep learning may enhance our ability to identify important genotype–phenotype associations in fibrotic lung diseases.

Abstract Background Across complex traits, common variants explain only a modest amount of variance, with SNP-heritability consistently below heritability estimates from close relatives. Here, we examined the contribution of rare variant to tobacco use risk in up to 26,000 individuals of European ancestry in the Trans-Omics for Precision Medicine (TOPMed) program with whole genome sequence (WGS;~30X coverage).Method We grouped about 35 million genetic variants by their minor allele frequencies (MAF) and linkage disequilibrium (LD) and estimated SNP-heritability for age of smoking initiation (N = 14,747), cigarettes smoked per day (N = 15,425), smoking cessation (N = 17,871) and initiation (N = 26,340) using linear mixed model. Rare variant population structure is detected and adjusted for by permutation procedure. We estimated an upper bound for narrow-sense heritability for tobacco use using available pedigrees consisting of close relatives in TOPMed.Results Rare variants with MAF 0.1–0.01%, mostly from non-protein altering region, accounted for 26% of variation in age of initiation and 15% for cessation. Follow-up analysis indicated that about one-third of these rare variants contribtion is potentially confounded with rare variants structure even after adjusting for principal components. After further conservative adjustment of population structure, we estimated SNP-based heritability to be 0.21 (SE = 0.08) for age of initiation, 0.15 (0.06) for cigarettes per day, 0.21 (0.09) for cessation, and 0.24 (0.07) for initiation, 1.8–4.5 times higher than previous SNP-based estimates. Our pedigree-based upper-bound for SNP-based heritability ranged from 0.18–0.35.Conclusion The substantial contribution of rare variants for several smoking phenotypes sheds light on the missing heritability and genetic etiology of tobacco use. It also informs fine-mapping strategies since the majority of the rare variant contribution was located in non-coding regulatory regions.

Abstract Rationale Despite shared environmental exposures, idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease are usually studied in isolation, and the presence of shared molecular mechanisms is unknown. Objectives We applied an integrative genomic approach to identify convergent transcriptomic pathways in emphysema and IPF. Methods We defined the transcriptional repertoire of chronic obstructive pulmonary disease, IPF, or normal histology lungs using RNA-seq (n = 87). Measurements and Main Results Genes increased in both emphysema and IPF relative to control were enriched for the p53/hypoxia pathway, a finding confirmed in an independent cohort using both gene expression arrays and the nCounter Analysis System (n = 193). Immunohistochemistry confirmed overexpression of HIF1A, MDM2, and NFKBIB members of this pathway in tissues from patients with emphysema or IPF. Using reads aligned across splice junctions, we determined that alternative splicing of p53/hypoxia pathway–associated molecules NUMB and PDGFA occurred more frequently in IPF or emphysema compared with control and validated these findings by quantitative polymerase chain reaction and the nCounter Analysis System on an independent sample set (n = 193). Finally, by integrating parallel microRNA and mRNA-Seq data on the same samples, we identified MIR96 as a key novel regulatory hub in the p53/hypoxia gene-expression network and confirmed that modulation of MIR96 in vitro recapitulates the disease-associated gene-expression network. Conclusions Our results suggest convergent transcriptional regulatory hubs in diseases as varied phenotypically as chronic obstructive pulmonary disease and IPF and suggest that these hubs may represent shared key responses of the lung to environmental stresses.